Thanks a lot Sebastian, Ted and Dmitriy, I'll try Giraph for a
performance benchmark.
You are right, power iteration is just the most simple form of Lanczos,
it shouldn't be in the scope.
On Tue 18 Feb 2014 03:59:57 AM EST, Sebastian Schelter wrote:
You can also use giraph for a superfast Page
You can also use giraph for a superfast PageRank implementation. Giraph
even runs on standard hadoop clusters.
Pagerank is usually computed by power iteration, which is much simpler than
lanczos or ssvd and only gives the eigenvector associated with the largest
eigenvalue.
Am 18.02.2014 09:33 schr
Really? I guess PageRank in mahout was removed due to inherited network
bottleneck of mapreduce. But I didn't know MLlib has the implementation.
Is mllib implementation based on Lanczos or SSVD? Just curious...
On 17/02/2014 11:11 PM, Dmitriy Lyubimov wrote:
I bet page rank in mllib in spark f
I bet page rank in mllib in spark finds stationary distribution much faster.
On Feb 17, 2014 1:33 PM, "peng" wrote:
> Agreed, and this is the case where Lanczos algorithm is obsolete.
> My point is: if SSVD is unable to find the eigenvector of asymmetric
> matrix (this is a common formulation of
Agreed, and this is the case where Lanczos algorithm is obsolete.
My point is: if SSVD is unable to find the eigenvector of asymmetric
matrix (this is a common formulation of PageRank, and some random
walks, and many other things), then we still have to rely on
large-scale Lanczos algorithm.
For the symmetric case, SVD is eigen decomposition.
On Mon, Feb 17, 2014 at 1:12 PM, peng wrote:
> If SSVD is not designed for such eigenvector problem. Then I would vote
> for retaining the Lanczos algorithm.
> However, I would like to see the opposite case, I have tested both
> algorithms o
If SSVD is not designed for such eigenvector problem. Then I would vote
for retaining the Lanczos algorithm.
However, I would like to see the opposite case, I have tested both
algorithms on symmetric case and SSVD is much faster and more accurate
than its competitor.
Yours Peng
On Wed 12 Feb
In PageRank I'm afraid I have no other option than eigenvector \lambda,
but not singular vector u & v:) The PageRank in Mahout was removed with
other graph-based algorithm.
On Tue 11 Feb 2014 06:34:17 PM EST, Ted Dunning wrote:
SSVD is very probably better than Lanczos for any large decomposit
SSVD is very probably better than Lanczos for any large decomposition.
That said, it does SVD, not eigen decomposition which means that the
question of symmetrical matrices or positive definiteness doesn't much
matter.
Do you really need eigen-decomposition?
On Tue, Feb 11, 2014 at 2:55 PM, pe
Just asking for possible replacement of our Lanczos-based PageRank
implementation. - Peng
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